renal masses
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Author(s):  
Fabrizio Gallo ◽  
Simone Sforza ◽  
Lorenzo Luciani ◽  
Daniele Mattevi ◽  
Paolo Barzaghi ◽  
...  

2022 ◽  
Vol 3 ◽  
Author(s):  
Niranjan J. Sathianathen ◽  
Nicholas Heller ◽  
Resha Tejpaul ◽  
Bethany Stai ◽  
Arveen Kalapara ◽  
...  

Purpose: Clinicians rely on imaging features to calculate complexity of renal masses based on validated scoring systems. These scoring methods are labor-intensive and are subjected to interobserver variability. Artificial intelligence has been increasingly utilized by the medical community to solve such issues. However, developing reliable algorithms is usually time-consuming and costly. We created an international community-driven competition (KiTS19) to develop and identify the best system for automatic segmentation of kidneys and kidney tumors in contrast CT and report the results.Methods: A training and test set of CT scans that was manually annotated by trained individuals were generated from consecutive patients undergoing renal surgery for whom demographic, clinical and outcome data were available. The KiTS19 Challenge was a machine learning competition hosted on grand-challenge.org in conjunction with an international conference. Teams were given 3 months to develop their algorithm using a full-annotated training set of images and an unannotated test set was released for 2 weeks from which average Sørensen-Dice coefficient between kidney and tumor regions were calculated across all 90 test cases.Results: There were 100 valid submissions that were based on deep neural networks but there were differences in pre-processing strategies, architectural details, and training procedures. The winning team scored a 0.974 kidney Dice and a 0.851 tumor Dice resulting in 0.912 composite score. Automatic segmentation of the kidney by the participating teams performed comparably to expert manual segmentation but was less reliable when segmenting the tumor.Conclusion: Rapid advancement in automated semantic segmentation of kidney lesions is possible with relatively high accuracy when the data is released publicly, and participation is incentivized. We hope that our findings will encourage further research that would enable the potential of adopting AI into the medical field.


Radiographics ◽  
2022 ◽  
Vol 42 (1) ◽  
pp. E33-E33
Author(s):  
Nicola Schieda ◽  
Matthew S. Davenport ◽  
Satheesh Krishna ◽  
Elizabeth A. Edney ◽  
Ivan Pedrosa ◽  
...  

2022 ◽  
Vol 15 (1) ◽  
pp. e246375
Author(s):  
Himanshu Pruthi ◽  
Harish Bhujade ◽  
Reetu Kundu ◽  
Srinivasa GY

Mesenchymal chondrosarcoma (MC) is a rare cartilaginous tumour that occurs in the extraskeletal locations in about one-third of cases. It is aggressive in behaviour and may involve the lower extremities, central nervous system or spine. Mesenchymal tumours are known for distant metastasis; however, metastasis to bilateral kidneys after treatment has not been reported earlier. We present a case of a soft-tissue intramuscular MC of the thigh in a 38-year-old patient which had been surgically excised after neoadjuvant chemotherapy. The patient presented with bilateral dense calcified renal masses after 6 years, which were cytologically proven as MC metastases. In the evaluation of bilateral calcified renal masses in patients with a history of MC, metastasis should be considered.


Author(s):  
Lorenzo Bianchi ◽  
Francesco Chessa ◽  
Pietro Piazza ◽  
Amelio Ercolino ◽  
Angelo Mottaran ◽  
...  

2021 ◽  
pp. 106194
Author(s):  
Vinson Wai-Shun Chan ◽  
Ahmad Abul ◽  
Filzah Hanis Osman ◽  
Helen Hoi-Lam Ng ◽  
Kaiwen Wang ◽  
...  

2021 ◽  
pp. 100118
Author(s):  
Corsetti Marco Antonio ◽  
González-Meza García Fernando ◽  
Mottaran Angelo ◽  
Sarchi Luca ◽  
Paciotti Marco ◽  
...  

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